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https://github.com/johnmath/snap-sp23
https://github.com/johnmath/snap-sp23
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/johnmath/snap-sp23
- Owner: johnmath
- License: mit
- Created: 2023-04-17T19:09:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-11T18:49:13.000Z (10 months ago)
- Last Synced: 2024-08-12T08:09:29.032Z (5 months ago)
- Language: Python
- Size: 9.89 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-MLSecOps - snap-sp23
README
# SNAP: Efficient Extraction of Private Properties with Poisoning
**Authors: Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan Ullman.**
Code for our [SNAP: Efficient Extraction of Private Properties with Poisoning](https://arxiv.org/pdf/2208.12348.pdf) paper that will appear at IEEE S&P 2023.
## Running the Model Confidence attack
This version of our attack obtains the model confidences from the target models for the distinguishing test. The **Label-Only** version of our attack where the target model returns only the predicted label can be found in the 'label-only' branch of our repository.
The following script modifies the training dataset, trains target and shadow models, runs the attack, and prints the results.
```shell
python run_attacks.py -dat [--dataset] -tp [--targetproperties] -t0 [--t0frac] -t1 [--t1frac] \
-sm [--shadowmodels] -p [--poisonlist] -d [--device] -fsub [--flagsub] \
-subcat [--subcategories] -q [--nqueries] -nt [--ntrials]
```
Each of the arguments can be set to one of the following:
```shell
dataset (string): "adult" -- Adult dataset
"census" -- Census-Income (KDD) dataset. (Link provided at the end to download the dataset).
targetproperties (string): An array representation of the list of target properties.
e.g. '[(race, White), (sex, Male)]'
t0frac (float): value between [0, 1] for t0 fraction of target property.
t1frac (float): value between [0, 1] for t1 fraction of target property. (t0 < t1)
shadowmodels (int): Number of shadow models per fraction. Default: 4.
poisonlist (string): An array representation of the list of poisoning rates as decimals (between 0 and 1).
e.g. '[0.03, 0.05]'
device (string): PyTorch device
e.g. "mps" (for Apple Silicon), "cpu", "cuda"
flagsub (bool): If True, runs the optimized version of SNAP that poisons a subproperty of the target property.
Make sure the original target property is large-sized (t0 > 0.1) for the optimized version.
subcategories (string): An array representation of the list of subproperties for the optimized version of SNAP.
e.g. '[(marital-status, Never-married)]'
nquereis (int): Number of black-box queries made to a target model. Default: 1000.
ntrials (int): The number of experimental trials to run. Default: 1.
```
An example to run **SNAP** attack on a **medium-sized** property :
```shell
python run_attack.py -tp="[(sex, Female),(occupation, Sales)]" -p="[0.006]" -t0=0.01 -t1=0.035
```
An example to run the optimized version of **SNAP** attack on **large-sized** property:
```shell
python run_attack.py -fsub=True -tp="[(race, White),(sex, Male)]" -subcat="[(marital-status, Never-married)]" -p="[0.03]" -t0=0.15 -t1=0.30
```
An example to run **Property Existence** attack on **small-sized** property:
```shell
python run_attack.py -tp="[(native-country, Germany]" -p="[0.0008]" -t0=0.0 -t1=0.001 -q 100
```
Link to Download Census: https://archive.ics.uci.edu/ml/datasets/Census-Income+(KDD). Download the dataset and place it in the 'dataset' folder.